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AI visibility tracking is a new category, and the measurement apparatus matters as much as the strategic decisions it informs. A prompt set that mirrors the business and a tag taxonomy that survives review beats a generic starter pack every time, and it survives the inevitable conversation with stakeholders who want to see the reasoning. This runs the full strategy build: a layered intake across igeo.ai, connected analytics and Search Console data, and the open web; a prescriptive prompt portfolio split by branded versus non-branded and sized to plan credits; a defensible brand roster, tag taxonomy, and topic structure written straight into igeo.ai; and an iteration loop that sharpens the strategy as live data comes in. A stakeholder presentation drops out at the end with the reasoning behind every decision, so the project doesn't just track the right things but also defend why.
Build our igeo.ai tracking strategy. Inspect our current igeo.ai project state, pull demand signals from connected data sources (analytics, Search Console, SEO tools), and recommend a defensible set of brands, topics, tags, and prompts to track — sized to our plan credits and split by branded vs non-branded. After my sign-off, write the changes directly to igeo.ai, analyse the results once the data settles, and iterate until the strategy stabilises. Then produce a stakeholder presentation that explains what we track and why.